Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "241" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 20 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 20 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459997 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.814617 | -1.007787 | -0.154164 | 0.115479 | -0.923183 | -0.652241 | 1.772263 | -1.481005 | 0.5910 | 0.6186 | 0.3957 | nan | nan |
| 2459996 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.703066 | -0.836319 | -0.397501 | 0.105986 | -1.092504 | -0.377326 | 0.670221 | -0.973510 | 0.6050 | 0.6273 | 0.4027 | nan | nan |
| 2459995 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.552314 | -1.101153 | -0.388217 | 0.083275 | -0.931551 | -0.640289 | 0.463709 | -1.045507 | 0.5973 | 0.6239 | 0.3912 | nan | nan |
| 2459994 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.663222 | -1.132763 | -0.340714 | 0.017318 | -1.065751 | -0.721104 | 0.677846 | -0.751023 | 0.5914 | 0.6175 | 0.3875 | nan | nan |
| 2459991 | RF_ok | 100.00% | 99.95% | 99.89% | 0.00% | - | - | 293.505892 | 292.932088 | inf | inf | 2978.503447 | 3030.123119 | 4510.775180 | 4594.185285 | 0.3808 | 0.3308 | 0.3079 | nan | nan |
| 2459990 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.166346 | -0.899221 | -0.249751 | 0.329122 | -0.736538 | -0.654593 | 0.415000 | -1.112466 | 0.5846 | 0.6106 | 0.3988 | nan | nan |
| 2459989 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.281994 | -1.056656 | -0.013851 | 0.250757 | -0.712530 | -1.084088 | 0.398517 | -0.722204 | 0.5858 | 0.6155 | 0.3968 | nan | nan |
| 2459988 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.237900 | -1.034175 | -0.285347 | 0.341255 | -0.797866 | -0.924214 | 0.495527 | -0.811040 | 0.5876 | 0.6170 | 0.3890 | nan | nan |
| 2459987 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.525443 | -1.147414 | -0.311598 | 0.042787 | -0.850520 | -0.941715 | 1.387174 | -0.328311 | 0.5896 | 0.6168 | 0.3903 | nan | nan |
| 2459986 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.228601 | -1.141406 | -0.338466 | 0.229351 | -1.013706 | -0.774569 | 0.134596 | -0.613492 | 0.6140 | 0.6431 | 0.3513 | nan | nan |
| 2459985 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.350882 | -1.120153 | -0.398036 | -0.009751 | -1.235085 | -1.165797 | 1.430036 | -1.123156 | 0.5959 | 0.6219 | 0.3922 | nan | nan |
| 2459984 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.373530 | -0.910886 | -0.305733 | 0.034325 | -1.126134 | -1.352200 | 0.159142 | -1.461503 | 0.6131 | 0.6383 | 0.3715 | nan | nan |
| 2459983 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.498216 | -1.112039 | -0.325160 | 0.264521 | -1.300952 | -0.930247 | 0.106769 | -0.943380 | 0.6218 | 0.6499 | 0.3431 | nan | nan |
| 2459982 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.131992 | -0.826313 | -0.734622 | -0.240905 | -0.550328 | -1.211096 | -1.029565 | -1.261200 | 0.6864 | 0.6861 | 0.2963 | nan | nan |
| 2459981 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.423560 | -1.106782 | -0.273468 | 0.459343 | -1.169290 | 0.338880 | 0.570467 | -0.847624 | 0.6028 | 0.6240 | 0.3904 | nan | nan |
| 2459980 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.626673 | -1.161758 | -0.532439 | 0.467478 | -1.313368 | -0.861302 | -0.731469 | -0.445992 | 0.6478 | 0.6621 | 0.3164 | nan | nan |
| 2459979 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.597203 | -1.256095 | -0.454654 | 0.497241 | -0.674903 | -0.939722 | 0.527431 | -0.840245 | 0.5935 | 0.6218 | 0.3944 | nan | nan |
| 2459978 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.505412 | -1.127040 | -0.324721 | 0.749555 | -0.661532 | -0.459771 | 1.172423 | -0.659646 | 0.5947 | 0.6208 | 0.4006 | nan | nan |
| 2459977 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.365457 | -0.906110 | -0.427356 | 0.499215 | -1.235402 | -0.652371 | 0.700600 | -1.296359 | 0.5589 | 0.5830 | 0.3605 | nan | nan |
| 2459976 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.335666 | -1.059492 | -0.459341 | 0.667858 | -0.582460 | -0.384091 | 0.773207 | -0.707853 | 0.6033 | 0.6276 | 0.3888 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 241 | N19 | RF_ok | ee Temporal Discontinuties | 1.772263 | -0.814617 | -1.007787 | -0.154164 | 0.115479 | -0.923183 | -0.652241 | 1.772263 | -1.481005 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 241 | N19 | RF_ok | ee Temporal Discontinuties | 0.670221 | -0.703066 | -0.836319 | -0.397501 | 0.105986 | -1.092504 | -0.377326 | 0.670221 | -0.973510 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 241 | N19 | RF_ok | ee Temporal Discontinuties | 0.463709 | -0.552314 | -1.101153 | -0.388217 | 0.083275 | -0.931551 | -0.640289 | 0.463709 | -1.045507 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 241 | N19 | RF_ok | ee Temporal Discontinuties | 0.677846 | -0.663222 | -1.132763 | -0.340714 | 0.017318 | -1.065751 | -0.721104 | 0.677846 | -0.751023 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 241 | N19 | RF_ok | ee Power | inf | 293.505892 | 292.932088 | inf | inf | 2978.503447 | 3030.123119 | 4510.775180 | 4594.185285 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 241 | N19 | RF_ok | ee Temporal Discontinuties | 0.415000 | -0.899221 | -0.166346 | 0.329122 | -0.249751 | -0.654593 | -0.736538 | -1.112466 | 0.415000 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 241 | N19 | RF_ok | ee Temporal Discontinuties | 0.398517 | -1.056656 | -0.281994 | 0.250757 | -0.013851 | -1.084088 | -0.712530 | -0.722204 | 0.398517 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 241 | N19 | RF_ok | ee Temporal Discontinuties | 0.495527 | -1.034175 | -0.237900 | 0.341255 | -0.285347 | -0.924214 | -0.797866 | -0.811040 | 0.495527 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 241 | N19 | RF_ok | ee Temporal Discontinuties | 1.387174 | -0.525443 | -1.147414 | -0.311598 | 0.042787 | -0.850520 | -0.941715 | 1.387174 | -0.328311 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 241 | N19 | RF_ok | nn Power | 0.229351 | -1.141406 | -0.228601 | 0.229351 | -0.338466 | -0.774569 | -1.013706 | -0.613492 | 0.134596 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 241 | N19 | RF_ok | ee Temporal Discontinuties | 1.430036 | -1.120153 | -0.350882 | -0.009751 | -0.398036 | -1.165797 | -1.235085 | -1.123156 | 1.430036 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 241 | N19 | RF_ok | ee Temporal Discontinuties | 0.159142 | -0.373530 | -0.910886 | -0.305733 | 0.034325 | -1.126134 | -1.352200 | 0.159142 | -1.461503 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 241 | N19 | RF_ok | nn Power | 0.264521 | -1.498216 | -1.112039 | -0.325160 | 0.264521 | -1.300952 | -0.930247 | 0.106769 | -0.943380 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 241 | N19 | RF_ok | nn Power | -0.240905 | -1.131992 | -0.826313 | -0.734622 | -0.240905 | -0.550328 | -1.211096 | -1.029565 | -1.261200 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 241 | N19 | RF_ok | ee Temporal Discontinuties | 0.570467 | -1.106782 | -1.423560 | 0.459343 | -0.273468 | 0.338880 | -1.169290 | -0.847624 | 0.570467 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 241 | N19 | RF_ok | nn Power | 0.467478 | -1.161758 | -1.626673 | 0.467478 | -0.532439 | -0.861302 | -1.313368 | -0.445992 | -0.731469 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 241 | N19 | RF_ok | ee Temporal Discontinuties | 0.527431 | -1.597203 | -1.256095 | -0.454654 | 0.497241 | -0.674903 | -0.939722 | 0.527431 | -0.840245 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 241 | N19 | RF_ok | ee Temporal Discontinuties | 1.172423 | -1.127040 | -1.505412 | 0.749555 | -0.324721 | -0.459771 | -0.661532 | -0.659646 | 1.172423 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 241 | N19 | RF_ok | ee Temporal Discontinuties | 0.700600 | -1.365457 | -0.906110 | -0.427356 | 0.499215 | -1.235402 | -0.652371 | 0.700600 | -1.296359 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 241 | N19 | RF_ok | ee Temporal Discontinuties | 0.773207 | -1.059492 | -1.335666 | 0.667858 | -0.459341 | -0.384091 | -0.582460 | -0.707853 | 0.773207 |